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cs.LG2026
Lightning OPD: Efficient Post-Training for Large Reasoning Models with Offline On-Policy Distillation
Yecheng Wu, Song Han, Han Cai +1
On-policy distillation (OPD) is an effective post-training paradigm for large language models but requires a live teacher server throughout training, resulting in substantial infra…
cs.LG2026
Quantization-Aware Distillation for NVFP4 Inference Accuracy Recovery
Meng Xin, Sweta Priyadarshi, Jingyu Xin +26
This technical report presents quantization-aware distillation (QAD) and our best practices for recovering accuracy of NVFP4-quantized large language models (LLMs) and vision-langu…
cs.LG2026
Stable Asynchrony: Variance-Controlled Off-Policy RL for LLMs
Luke J. Huang, Zhuoyang Zhang, Qinghao Hu +2
Asynchronous reinforcement learning has become increasingly central to scaling LLM post-training, delivering major throughput gains by decoupling rollout generation from policy upd…